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Improving detection and classification of diabetic retinopathy using CUDA and Mask RCNN

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Abstract

Diabetic retinopathy (DR) is an eye disease caused by diabetes and can progress to certain degrees. Because DR’s the final stage can cause blindness, early detection is crucial to prevent visual disturbances. With the development of GPU technology, image classification and object detection can be done faster. Particularly on medical images, these processes play an important role in disease detection. In this work, we improved our previous work to detect diabetic retinopathy using Faster RCNN and attention layer. In the detection phase, firstly non-used area of DR image was extracted using compute unified device architecture with gradient-based edge detection method. Then Mask RCNN was used instead of faster region-based convolutional neural networks (Faster RCNN) to detect lesion areas more successful. With the proposed method, more successful results were obtained than the our previous work in DenseNet, MobileNet and ResNet networks. In addition, more successful results were obtained than other works in the literature in ACC and AUC metrics obtained by using VGG19.

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Data availability

Previously reported diabetic retinopathy datasets were used to support this study and are available at https://www.adcis.net/en/third-party/messidor/, https://www.kaggle.com/c/diabetic-retinopathy-detection/data, https://www.it.lut.fi/project/imageret/diaretdb0/, https://www.it.lut.fi/project/imageret/diaretdb1/, and https://ieee-dataport.org/open-access/indian-diabetic-retinopathy-image-dataset-idrid. These datasets are cited at relevant places within the text as references [41–44].

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Acknowledgements

We thank the editors, reviewers, and all cited authors.

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Authors and Affiliations

  1. Department of Computer Engineering, Gazi University, Ankara, Turkey

    Abdüssamed Erciyas, Necaattin Barışçı & Hüseyin Polat

  2. Department of Computer Engineering, Kırıkkale University, Kırıkkale, Turkey

    Halil Murat Ünver

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  2. Necaattin Barışçı
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  3. Halil Murat Ünver
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Contributions

N.B. and A.E. designed the project, main conceptual ideas and project outline. A.E. solved all the technical details and did the numerical calculations for the proposed method and got the results. N.B., H.M.Ü., and H.P. took the lead in writing the article. A.E and N.B. wrote the article template. H.M.Ü and H.P. helped moderate the article. All authors provided critical feedback and assisted in research, analysis, and finalization of the manuscript. All authors discussed the results and contributed to the final article.

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Correspondence to Abdüssamed Erciyas.

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Erciyas, A., Barışçı, N., Ünver, H.M. et al. Improving detection and classification of diabetic retinopathy using CUDA and Mask RCNN. SIViP 17, 1265–1273 (2023). https://doi.org/10.1007/s11760-022-02334-9

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